Global gene expression profiling confirms the molecular fidelity of primary tumor-based orthotopic xenograft mouse models of medulloblastoma

Abstract

We previously showed that primary tumor-based orthotopic xenograft mouse models of medulloblastoma replicated the histopathological phenotypes of patients' original tumors. Here, we performed global gene expression profiling of 11 patient-specific xenograft models to further determine whether the xenograft tumors were molecularly accurate during serial subtransplantations in mouse brains and whether they represented all the molecular subtypes of medulloblastoma that were recently described. Analysis of the transcriptomes of 9 pairs of matched passage I xenografts and patients' tumors revealed high correlation coefficients (r(2) > 0.95 in 5 models, > 0.9 in 3 models, and > 0.85 in 1 model) and only identified 69 genes in which expressions were altered (FDR = 0.0023). Subsequent pair-wise comparisons between passage I, III, and V xenografts from the 11 models further showed that no dramatic alterations were introduced (r(2) > 0.9 in 8 models and > 0.8 in 3 models). The genetic abnormalities of each model were then identified through comparison with control RNAs from 5 normal cerebella and 2 fetal brains. Hierarchical clustering using 3 previously published molecular signatures showed that our models span the whole spectrum of molecular subtypes, including SHH (n = 2), WNT (n = 2), and the most recently identified group C (n = 4) and group D (n = 3). In conclusion, we demonstrated that the 11 orthotopic medulloblastoma xenograft models were molecularly faithful to the primary tumors, and our comprehensive collection of molecularly distinct animal models should serve as a valuable resource for the development of new targeted therapies for medulloblastoma.

Figures

Fig. 1.

Summary of correlation coefficients using 6775 genes that have the detection P < .001 in all 94 arrays. (A) Hierarchical clustering of overall correlation in the 9 models with high correlation coefficients (r2 > 0.9). The value of r2 was used as a metric for building the dendrogram. The dendrogram at the top of the figure shows the grouping of the samples. The length of the legs is indicative of the correlation among the samples. The shorter the legs, the closer they are. Patients' tumors (Pt) and intracerebellar (ICb) xenograft tumors during serial subtransplanation from passage I (I) to passage V (V) were compared with normal brain tissue. (B) Graph highlighting the correlation either between xenograft tumors and their corresponding patients' tumors or between serially passaged xenograft tumors (passage III and V) with the passage I xenograft tumors when patients' tumors were not available (case #984 and #1197).

Fig. 2.

Gene expression and pathway analysis of 11 medulloblastomas during transition from patients to growth in SCID mice. (A) The list of the numbers of differentially expressed genes from patients' tumors (Patient) to passage I (P-I) xenograft, and during serial subtransplantations up to passage V (P-V)xenograft tumors. (B) Hierarchical clustering of the 69 differentially expressed genes during transition from the patient to passage I xenograft at the false discovery rate (FDR) of 0.0006.

Fig. 3.

Hierarchical clustering showing the classification of 11 xenograft models (passage III) using previously reported gene signatures of medulloblastomas. (A) Clustering using 1500 most significant genes from Thompson et al. (2006) study. Color legend: Red (Group A), Blue (Group B), Green (Group C), Aquamarine (Group D), Black (Group E). (B) Clustering using 1500 most significant genes from Kool et al. (2008) study. Color legend: Red (Group A), Blue (Group B), Green (Group C), Aquamarine (Group D), Black (Group E). (C) Classification using the genes provided in the supplemental material from the Northcott et al. (2010) study. All 100 genes provided in the supplemental material were used, and 88 genes from our Illumina platform were successfully used to discriminate between the 4 types of medulloblastomas.

Fig. 4.

Molecular subclassification of the 11 xenograft models using the genetic identifiers identified by the Northcott et al. study. (A) Clustering of the 11 xenograft models at passage III with the identified classifiers from gene expression profiling results. For each sample, results from the duplicated array hybridization were presented. Genes highlighted in red were selected for qRT-PCR validation. (B) Validation of differentially expressed subclassifiers with quantitative RT-PCR. Total RNAs from 4 adult cerebella (NCb, A91105, A508112, and A508285) and 2 fetal brains (B111143 and A602127) were included.